
ESC-50[1]}: a sound classification with 50 environmental sound classes. We conduct leave-one-out cross-validation (LOOCV) with the official five folds.

UrbanSound8K[2]} (US8K): a sound classification with ten urban sound classes. We conduct LOOCV with the official ten folds.

FSD50K[3]}: a multi-label classification SER task with 200 classes drawn from AudioSet ontology that covers a wide variety of sound events.

Speech Commands V2[4]} (SPCV2): a speech command word classification task, containing 105,829 utterances from 2,618 speakers.

VoxCeleb1[5]} (VC1): a dataset consisting of interview videos of celebrities. We use this as a speaker identification task of audio samples.

VoxForge[6]} (VF): a language identification task: German, English, Spanish, French, Italian, and Russian.

CREMA-D[7]} (CRM-D): a dataset consisting of facial and vocal emotional expressions. We use audio data as a speech emotion recognition (SER) task of six classes. We assign 70 % of speakers (63) as training, 10 % (9) as validation, and the remaining 20 % (19) as test splits, with no speaker duplication in multiple splits.

GTZAN[8]}: a music genre recognition task with
ten music genre classes. We follow fault-filtered partitioning[9]}[10]}.

NSynth[11]}: a large collection of musical notes. We use this as an instrument family classification task.

Pitch Audio Dataset (Surge synthesizer)[12]} (Surge): a pitch audio classification task composed of 88 MIDI note classes and 2,084 tone preset sounds. We assign 10 % of the presets as validation, another 10 % as test, and the remaining 80 % as a training set.

